Analyzing the Role of Community and Individual Factors in LAMP Grant Funding: Identifying Diverse Barriers Across Clustered US Counties

FAS Food Systems Impact Fellowship Capstone Project, April 2024

Author

Elliot Hohn Sr. Agricultural Data Scientist Impact Fellow

Introduction

Local Agriculture Market Program (LAMP)

The USDA’s Agricultural Marketing Service (AMS) administers a variety of grant programs aimed at strengthening local and regional food systems. The Local Agriculture Market Program (LAMP) is one such program that supports direct producer-to-consumer marketing, food enterprises, and value-added agricultural products. Established under the 2018 Farm Bill, LAMP fosters community collaboration and public-private partnerships to improve regional food economies, aiding in the development of business strategies and infrastructure for local food systems. The Farm Bill provided LAMP $50 million per year in mandatory funding and the programs received significant supplemental funding through the Consolidated Appropriations Act of 2021 and the American Rescue Plan of 2021.1 The major grant programs within LAMP include the Local Food Promotion Program (LFPP), Regional Food Systems Partnership (RFSP), and the Farmers Market Promotion Program (FMPP).

Promotional materials for LAMP. Image: USDA-AMS, 2024

Building community capital through food systems investment

Allocating grant funding

The goals of the LAMP program include: (1) simplify the application processes and the reporting processes for the Program; (2) improve income and economic opportunities for producers and food businesses through job creation; and (3) strengthen capacity and regional food system development through community collaboration and expansion of mid-tier value chains.2

Each program within LAMP includes a set of constraints intended to improve the allocation of resources to specific program activity areas.

In 2021, AMS partnered with Florida A&M University and the University of Maryland Eastern Shore on a project focusing on the following goals3:

  1. Evaluate barriers to AMS grant opportunities for socially disadvantaged communities

  2. Invest in building trust and confidence between these communities and the USDA

  3. Take action to rectify inequalities in program access through targeted outreach, training, and technical assistance.

The results of this work are intended to be used to improve access and reduce barriers for all applicants, presumably part of the agency’s renewed efforts to address USDA’s history of systemic discrimination.4

Community preparedness

Recent research suggests that the success of food system interventions, policies, and strategies for local economic development may hinge on the preexisting levels of community capital.5

Additional research showed positive associations between cultural and social capital and farm to school activity.6

Much of this research highlights community assets that are often overlooked in community development work.7

Objective

This report intends to lay the groundwork for an analytic approach that helps determine which community characteristics are associated with LAMP grant funding allocation. This could help determine if there is something akin to a “threshold of community preparedness” the unknowingly results in certain low-resource communities being excluded from LAMP programming. If so, the results of this research could provide insight into the particular characteristics associated with LAMP access, which could help agency staff to better allocate resources to ensure equitable access to grant funds.

Methods

Data access and aggregation

As a first step, a variety of data sets were obtained, cleaned, organized, and used for general data exploration. Information on specific datasets and sources can be found below. All work was done using the open source statistical software R version 4.4.0.8

LAMP grant data

Information on LAMP awards came from the LAMP Navigator website, where AMS has made this information publicly available, along with a dashboard for sorting, filtering, and visualizing the grant information.9 Along with information about the organizations receiving the grant, the dataset includes information on the purpose of the grant (e.g., technical assistance, infrastructure, processing), the match amount, and the total project cost.

LAMP grant award amounts, 2006 - 2023
Each green dash represents a single grant award

Geographic distribution of LAMP Grants, 2006-2023

Community characteristics

A variety of socioeconomic and environmental factors were investigated to assess how they may influence the likelihood of receiving a LAMP grant. These factors include indicators of community wealth, which encompasses social capital, natural capital, financial capital, and a variety of other forms of wealth, which have been shown impacts the ability to engage and participate in such programs.10 Additionally, it includes factors related to poverty and food security, which have been shown to exacerbate vulnerabilities and influence accessibility and participation in programs.11 Finally, considering the food systems-focus of LAMP, factors related to urbanization and proximity to agricultural land were included because they can influence market dynamics and food system connectivity.12

Indicators of community wealth

Community wealth data were accessed via the USDA AMS Data and Metrics GitHub repository.13 The main source of data was the “Indicators of Community Wealth” dataset within this repository, which was the result of various pre-processing steps that are outlined within the Rmarkdown file included in the repo.

Indicators of community wealth variables
Descriptions and sources of data used in analysis
Description Data Source
Demographics
racial_div Constructed racial diversity index from 0 (no diversity) to 10 (complete diversity), 2010 U.S. Census Bureau, Modified Race Data (2010)
insured Percent of population with health insurance Robert Wood Johnson Foundation, County Health Rankings
health_factors Health Factors Z-Score Robert Wood Johnson Foundation, County Health Rankings
health_outcomes Health Outcome Z-Score Robert Wood Johnson Foundation, County Health Rankings
Labor
create_jobs Percent of workforce employed in the arts USDA Economic Research Service, Creative Class Codes
Institutions
ed_attain Percent of adult population with at least a Bachelor's degree U.S. Census Bureau, American Community Survey, table S1501
Food Access
food_secure Percent of population food secure Feeding America Map the Meal Gap
Processing & Distribution
foodbev_est_CBP Food and beverage manufacturing establishments per 10,000 people U.S. Census Bureau, County Business Patterns
est_CBP Other manufacturing establishments per 10,000 people U.S. Census Bureau, County Business Patterns
Community Characteristics
highway_km Inverse of population-weighted distance (km) to nearest interstate highway ramp Dicken et al. (2011)
broad_16 Percent of population with access to fixed advanced telecomm FCC (2016)
pc1b_manufacturing Constructed index derived from a prinicipal component analysis including food and beverage establishments, and other manufacturing establishments Derived in Schmitt et al. (2021)
pc2b_infrastructure Constructed index derived from a prinicipal component analysis including percent of population with access to telecommunications, and proximity to highway ramp Derived in Schmitt et al. (2021)
create_indus Creative industry businesses per 100,000 population, 2014 Kushner & Cohen, Local Arts Index (2018)
pub_lib Public libraries per 100,000 people Kushner & Cohen, Local Arts Index (2018)
museums Museums per 100,000 people Kushner & Cohen, Local Arts Index (2018)
pc1c_artsdiversity Constructed index derived from a prinicipal component analysis including percent of workforce employed in the arts, and racial diversity index Derived in Schmitt et al. (2021)
pc2c_creativeindustries Constructed index derived from a prinicipal component analysis including public libraries per 100,000 people, creative industry businesses per 100,000 people, and museums per 100,000 people. Derived in Schmitt et al. (2021)
localgovfin Cash and security holdings less government debt per capita U.S. Census Bureau, Annual survey of state and local government finance. Historical data (formerly Special 60). File: “_IndFin_1967-2012”
owner_occupied Owner-occupied units without a mortgage per capita U.S. Census Bureau, American Community Survey, table S2507
deposits Bank deposits per capita at FDIC-insured institutions FDIC, Deposit Market Share Reports - Summary of Deposits
pc1f Financial capital - financial solvency Derived in Schmitt et al. (2021)
primary_care Number of primary care physicians per 10,000 population Robert Wood Johnson Foundation, County Health Rankings
pc1h_healtheducation Constructed index derived from a prinicipal component analysis of human capital data including educational attinment, health facor and outcome score from the Robert Wood Johnson Foundation Derived in Schmitt et al. (2021)
pc2h_medicalfoodsecurity Constructed index derived from a prinicipal component analysis of human capital data including percent of population food secure, percent of population with health insurance, and number of primary care phsyicans per 10,000. Derived in Schmitt et al. (2021)
natamen_scale Natural Amenities Scale McGranahan, D., 1999. Natural Amenities Scale. U.S. Department of Agriculture, Economic Research Service
prime_farmland Percent of farmland acres designated as prime farmland, 2012 U.S. Department of Agriculture, Natural Resource Conservation Service (USDA NRCS). 2012. National Resources Inventory
conserve_acre Percent of total acres with conservation easement, 2016 National Conservation Easement Database (NCED), 2016.
acre_FSA Percent of total acres in conservation-related programs and woodlands U.S. Department of Agriculture, Farm Service Agency (USDA FSA). 2017. FSA Crop Acreage Data
acre_NFS Percent of total acres in National Forests U.S. Forest Service (USFS). 2017. Land areas of the National Forest System. FS-383.
pc1n_naturalamenitiesconservation Constructed index derived from a prinicipal component analysis including natural amenity scale and share of acres in National Forest Derived in Schmitt et al. (2021)
pc2n_farmland Constructed index derived from a prinicipal component analysis including prime farmland Derived in Schmitt et al. (2021)
pvote Percent of eligible voters that voted Rupasingha, Goetz, and Freshwater (2006) and 2017 data updates
nccs Number of nonprofit organizations per 1,000 population Rupasingha, Goetz, and Freshwater (2006) and 2017 data updates
assn Number of social establishments per 1,000 population Rupasingha, Goetz, and Freshwater (2006) and 2017 data updates
respn U.S. Population Census response rate, percent Rupasingha, Goetz, and Freshwater (2006) and 2017 data updates
pc1s_nonprofitsocialindustries Constructed index derived from a prinicipal component analysis including number of social establishments and nonprofits per capita Derived in Schmitt et al. (2021)
pc2s_publicvoiceparticipation Constructed index derived from a prinicipal component analysis including public voice and participation Derived in Schmitt et al. (2021)

Distribution of community wealth metrics

Additional community characteristics

Poverty rate

Count-level poverty rates were sourced from the 2022 US Census.14

Farmland proportion

Data on total agricultural land were obtained from the 2022 Census of Agriculture.15

“Rural” and “underserved” classification

Data on county-level classification as “rural” and/or “underserved” were obtained from the Consumer Financial Protection Bureau.16

Rural-Urban Continuum Classification

The categorization of each county on the rural-urban continuum came from the USDA-ERS.17

Exploratory map of additional explanatory variables

Analyzing the relationship between community characteristics and LAMP funding

The USDA aims to make LAMP grants available to all eligible communities across the US and its territories. To effectively distribute these grants, USDA considers various factors, such as potential for job creation, improvement in local food supply chains, and enhancement of local economies, with an intended focus on underserved communities and regions with limited market access.

This analysis

Plotting each variable against total LAMP award money received

Dimension reduction with PCA

Started with 44 variables, which were reduced to 10 using a principal components analysis.

Principal components

Cluster analysis

Cluster exploration

Dimension reduction

Use principal component analysis (PCA) to reduce dimensionality of datasets and retain only most important information.

Regression


z test of coefficients:

                                        Estimate     Std. Error z value
(Intercept)                         -16.34765283     3.33147022 -4.9070
acre_FSA                             59.53579570    10.87265019  5.4757
acre_NFS                             -0.78471684     0.42095503 -1.8641
assn                                  0.04981713     0.19344597  0.2575
broad_16                              0.00702341     0.35937781  0.0195
conserve_acre                        -5.52451801     1.56989617 -3.5190
create_indus                          0.00117979     0.00091328  1.2918
create_jobs                           2.10974070     1.18958897  1.7735
deposits                             -0.00057738     0.00032605 -1.7708
ed_attain                             5.15896334     0.57604639  8.9558
est_CBP                               0.01118941     0.00727301  1.5385
food_secure                          -0.82165930     1.54167679 -0.5330
foodbev_est_CBP                       0.06126988     0.02167532  2.8267
health_factors                        0.02718897     0.14978911  0.1815
health_outcomes                       0.34380960     0.10240106  3.3575
highway_km                            0.46972150     0.81929975  0.5733
insured                              -0.97855661     0.96601464 -1.0130
localgovfin                           0.03854105     0.02131235  1.8084
museums                              -0.00348753     0.00323234 -1.0789
natamen_scale                         0.33905470     0.04038496  8.3956
nccs                                  0.01424959     0.01506542  0.9458
owner_occupied                       -4.99456326     1.42072215 -3.5155
pc1b_manufacturing                   -0.03127380     0.02757354 -1.1342
pc1c_artsdiversity                    0.04901861     0.02514356  1.9495
pc1f                                 -0.00819300     0.01271917 -0.6441
pc1h_healtheducation                 -0.04812201     0.01110694 -4.3326
pc1n_naturalamenitiesconservation     0.01162760     0.00833250  1.3955
pc1s_nonprofitsocialindustries       -0.03872792     0.05322936 -0.7276
pc2b_infrastructure                   0.01312682     0.01839941  0.7134
pc2c_creativeindustries              -0.00692858     0.01242096 -0.5578
pc2h_medicalfoodsecurity              0.03058305     0.00690597  4.4285
pc2n_farmland                         1.73909872     0.27578230  6.3061
pc2s_publicvoiceparticipation         0.01362764     0.01660111  0.8209
primary_care                          0.02260320     0.01451776  1.5569
prime_farmland                    -2903.92863217   450.35463310 -6.4481
pub_lib                              -0.00439377     0.00280096 -1.5687
pvote                                -1.10215084     0.91114242 -1.2096
racial_div                            0.04782802     0.02349459  2.0357
respn                                -0.08226198     1.64593901 -0.0500
poverty_rate                          0.05715493     0.00889767  6.4236
ag_proportion                        -0.00399584     0.00115412 -3.4622
RUCC_20232                            0.21143107     0.08593408  2.4604
RUCC_20233                           -0.06778486     0.09858996 -0.6875
RUCC_20234                           -0.04734336     0.11809985 -0.4009
RUCC_20235                           -0.07538390     0.19168828 -0.3933
RUCC_20236                           -0.13130662     0.12801914 -1.0257
RUCC_20237                           -0.55404221     0.16748338 -3.3080
RUCC_20238                           -0.00325132     0.14661999 -0.0222
RUCC_20239                           -0.32250713     0.17483199 -1.8447
is_rural1                             0.00332886     0.10053566  0.0331
is_underserved1                      -0.36612456     0.17360294 -2.1090
                                               Pr(>|z|)    
(Intercept)                             0.0000009246186 ***
acre_FSA                                0.0000000435689 ***
acre_NFS                                      0.0623028 .  
assn                                          0.7967737    
broad_16                                      0.9844077    
conserve_acre                                 0.0004331 ***
create_indus                                  0.1964205    
create_jobs                                   0.0761452 .  
deposits                                      0.0765920 .  
ed_attain                         < 0.00000000000000022 ***
est_CBP                                       0.1239301    
food_secure                                   0.5940580    
foodbev_est_CBP                               0.0047029 ** 
health_factors                                0.8559634    
health_outcomes                               0.0007866 ***
highway_km                                    0.5664276    
insured                                       0.3110682    
localgovfin                                   0.0705458 .  
museums                                       0.2806103    
natamen_scale                     < 0.00000000000000022 ***
nccs                                          0.3442266    
owner_occupied                                0.0004389 ***
pc1b_manufacturing                            0.2567124    
pc1c_artsdiversity                            0.0512299 .  
pc1f                                          0.5194806    
pc1h_healtheducation                    0.0000147353281 ***
pc1n_naturalamenitiesconservation             0.1628797    
pc1s_nonprofitsocialindustries                0.4668787    
pc2b_infrastructure                           0.4755754    
pc2c_creativeindustries                       0.5769715    
pc2h_medicalfoodsecurity                0.0000094892957 ***
pc2n_farmland                           0.0000000002862 ***
pc2s_publicvoiceparticipation                 0.4117104    
primary_care                                  0.1194861    
prime_farmland                          0.0000000001133 ***
pub_lib                                       0.1167253    
pvote                                         0.2264185    
racial_div                                    0.0417801 *  
respn                                         0.9601393    
poverty_rate                            0.0000000001331 ***
ag_proportion                                 0.0005357 ***
RUCC_20232                                    0.0138787 *  
RUCC_20233                                    0.4917404    
RUCC_20234                                    0.6885117    
RUCC_20235                                    0.6941253    
RUCC_20236                                    0.3050427    
RUCC_20237                                    0.0009395 ***
RUCC_20238                                    0.9823082    
RUCC_20239                                    0.0650857 .  
is_rural1                                     0.9735859    
is_underserved1                               0.0349466 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

z test of coefficients:

                                        Estimate     Std. Error z value
(Intercept)                         -28.72747140     6.32673741 -4.5406
acre_FSA                            105.07711111    21.60506215  4.8635
acre_NFS                             -1.33190555     0.77509145 -1.7184
assn                                 -0.05642080     0.29941348 -0.1884
broad_16                             -0.22693119     0.64429482 -0.3522
conserve_acre                        -9.67361767     2.89169702 -3.3453
create_indus                          0.00261047     0.00153956  1.6956
create_jobs                           3.31215266     2.12364445  1.5597
deposits                             -0.00109281     0.00055997 -1.9516
ed_attain                             9.16976576     1.05223713  8.7145
est_CBP                               0.01579819     0.01276700  1.2374
food_secure                          -1.39185085     2.72438729 -0.5109
foodbev_est_CBP                       0.11164264     0.04066580  2.7454
health_factors                        0.05442879     0.28226148  0.1928
health_outcomes                       0.60924679     0.19422793  3.1368
highway_km                            0.47314941     1.47892100  0.3199
insured                              -1.74517942     1.73033805 -1.0086
localgovfin                           0.07418550     0.03845194  1.9293
museums                              -0.00638266     0.00592707 -1.0769
natamen_scale                         0.57320146     0.08035886  7.1330
nccs                                  0.01674013     0.02204030  0.7595
owner_occupied                       -8.98803647     2.46238711 -3.6501
pc1b_manufacturing                   -0.05979133     0.05465999 -1.0939
pc1c_artsdiversity                    0.07926327     0.04913879  1.6130
pc1f                                 -0.01096728     0.01955132 -0.5609
pc1h_healtheducation                 -0.08726308     0.02155036 -4.0493
pc1n_naturalamenitiesconservation     0.02176870     0.01551926  1.4027
pc1s_nonprofitsocialindustries       -0.03463015     0.07988998 -0.4335
pc2b_infrastructure                   0.03176661     0.03319547  0.9570
pc2c_creativeindustries              -0.01000155     0.02202076 -0.4542
pc2h_medicalfoodsecurity              0.05249454     0.01200295  4.3735
pc2n_farmland                         3.07473906     0.55320806  5.5580
pc2s_publicvoiceparticipation         0.01846217     0.02604873  0.7088
primary_care                          0.04827805     0.02514637  1.9199
prime_farmland                    -5158.58323405   902.28247288 -5.7173
pub_lib                              -0.00749105     0.00536223 -1.3970
pvote                                -2.25242416     1.63682633 -1.3761
racial_div                            0.07452809     0.04244050  1.7561
respn                                 0.13182044     2.57752228  0.0511
poverty_rate                          0.09871572     0.01596350  6.1838
ag_proportion                        -0.00663929     0.00208010 -3.1918
RUCC_20232                            0.34705752     0.15298740  2.2685
RUCC_20233                           -0.13983487     0.17426412 -0.8024
RUCC_20234                           -0.08606532     0.20694145 -0.4159
RUCC_20235                           -0.16427324     0.32446786 -0.5063
RUCC_20236                           -0.24712386     0.22663748 -1.0904
RUCC_20237                           -0.99280396     0.29572338 -3.3572
RUCC_20238                           -0.03929533     0.26413584 -0.1488
RUCC_20239                           -0.60561942     0.31369462 -1.9306
is_rural1                             0.03637833     0.17766039  0.2048
is_underserved1                      -0.78783961     0.34176474 -2.3052
                                               Pr(>|z|)    
(Intercept)                          0.0000056082275411 ***
acre_FSA                             0.0000011530397789 ***
acre_NFS                                      0.0857264 .  
assn                                          0.8505335    
broad_16                                      0.7246760    
conserve_acre                                 0.0008219 ***
create_indus                                  0.0899633 .  
create_jobs                                   0.1188414    
deposits                                      0.0509912 .  
ed_attain                         < 0.00000000000000022 ***
est_CBP                                       0.2159299    
food_secure                                   0.6094310    
foodbev_est_CBP                               0.0060443 ** 
health_factors                                0.8470913    
health_outcomes                               0.0017082 ** 
highway_km                                    0.7490223    
insured                                       0.3131775    
localgovfin                                   0.0536931 .  
museums                                       0.2815396    
natamen_scale                        0.0000000000009819 ***
nccs                                          0.4475394    
owner_occupied                                0.0002621 ***
pc1b_manufacturing                            0.2740087    
pc1c_artsdiversity                            0.1067339    
pc1f                                          0.5748329    
pc1h_healtheducation                 0.0000513791041393 ***
pc1n_naturalamenitiesconservation             0.1607096    
pc1s_nonprofitsocialindustries                0.6646712    
pc2b_infrastructure                           0.3385895    
pc2c_creativeindustries                       0.6496939    
pc2h_medicalfoodsecurity             0.0000122287961991 ***
pc2n_farmland                        0.0000000272858958 ***
pc2s_publicvoiceparticipation                 0.4784766    
primary_care                                  0.0548728 .  
prime_farmland                       0.0000000108255595 ***
pub_lib                                       0.1624128    
pvote                                         0.1687930    
racial_div                                    0.0790781 .  
respn                                         0.9592121    
poverty_rate                         0.0000000006256102 ***
ag_proportion                                 0.0014139 ** 
RUCC_20232                                    0.0232965 *  
RUCC_20233                                    0.4223039    
RUCC_20234                                    0.6774889    
RUCC_20235                                    0.6126565    
RUCC_20236                                    0.2755402    
RUCC_20237                                    0.0007873 ***
RUCC_20238                                    0.8817356    
RUCC_20239                                    0.0535323 .  
is_rural1                                     0.8377571    
is_underserved1                               0.0211548 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

z test of coefficients:

                      Estimate  Std. Error z value              Pr(>|z|)    
(Intercept)        -0.63406447  0.08303868 -7.6358   0.00000000000002245 ***
pc1_education       0.25073591  0.01389627 18.0434 < 0.00000000000000022 ***
pc2_arts            0.13861902  0.01894481  7.3170   0.00000000000025359 ***
pc3_conservation1  -0.08786720  0.01722017 -5.1026   0.00000033506543981 ***
pc4_farmland1       0.01590617  0.01885908  0.8434              0.398992    
pc5_infrastructure  0.23777957  0.04272613  5.5652   0.00000002618481430 ***
pc6_farmland2       0.02883481  0.03424762  0.8420              0.399815    
pc7_manufacturing   0.16520286  0.06709516  2.4622              0.013808 *  
pc8_conservation2  -0.03448819  0.02682045 -1.2859              0.198481    
pc9_foodbev         0.06498926  0.04512365  1.4402              0.149797    
pc10_civics         0.00069043  0.03890281  0.0177              0.985840    
RUCC_20232          0.24919648  0.07772021  3.2063              0.001344 ** 
RUCC_20233          0.06461295  0.08656441  0.7464              0.455417    
RUCC_20234          0.20189082  0.10883993  1.8549              0.063606 .  
RUCC_20235          0.12507406  0.18167368  0.6885              0.491167    
RUCC_20236         -0.03172156  0.11954595 -0.2654              0.790740    
RUCC_20237         -0.30605517  0.15555337 -1.9675              0.049123 *  
RUCC_20238          0.13149287  0.13809952  0.9522              0.341016    
RUCC_20239         -0.08910197  0.16311522 -0.5463              0.584893    
is_rural1          -0.01877293  0.09584299 -0.1959              0.844711    
is_underserved1    -0.25248643  0.15184672 -1.6628              0.096358 .  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Footnotes

  1. https://www.ams.usda.gov/sites/default/files/media/LAMP_Report_to_Congress.pdf↩︎

  2. https://www.ams.usda.gov/services/grants/lamp. Accessed April 20, 2024↩︎

  3. https://www.ams.usda.gov/sites/default/files/media/MSDUSDAAMSGrantApplicantTASociallyDisadvantaged.pdf↩︎

  4. Kashyap, Pratyoosh, Becca B.R. Jablonski, and Allison Bauman. “Exploring the Relationships among Stocks of Community Wealth, State Farm to School Policies, and the Intensity of Farm to School Activities.” Food Policy 122 (January 2024): 102570. https://doi.org/10.1016/j.foodpol.2023.102570.↩︎

  5. Schmit, Todd M., Becca B.R. Jablonski, Alessandro Bonanno, and Thomas G. Johnson. “Measuring Stocks of Community Wealth and Their Association with Food Systems Efforts in Rural and Urban Places.” Food Policy 102 (July 2021): 102119. https://doi.org/10.1016/j.foodpol.2021.102119.↩︎

  6. Kashyap, Pratyoosh, Becca B.R. Jablonski, and Allison Bauman. “Exploring the Relationships among Stocks of Community Wealth, State Farm to School Policies, and the Intensity of Farm to School Activities.” Food Policy 122 (January 2024): 102570. https://doi.org/10.1016/j.foodpol.2023.102570.↩︎

  7. Kashyap, Pratyoosh, Becca B.R. Jablonski, and Allison Bauman. “Exploring the Relationships among Stocks of Community Wealth, State Farm to School Policies, and the Intensity of Farm to School Activities.” Food Policy 122 (January 2024): 102570. https://doi.org/10.1016/j.foodpol.2023.102570.↩︎

  8. https://www.r-project.org/↩︎

  9. https://www.ams.usda.gov/data/lamp-navigator↩︎

  10. Flora, Cornelia Butler, Jan L. Flora, and Stephen P. Gasteyer. Rural Communities: Legacy and Change. 4th ed. Routledge, 2018. https://doi.org/10.4324/9780429494697.↩︎

  11. Alisha Coleman-Jensen, Matthew P. Rabbitt, Christian A. Gregory, and Anita Singh. 2021. Household Food Security in the United States in 2020, ERR-298, U.S. Department of Agriculture, Economic Research Service.↩︎

  12. Pothukuchi, Kameshwari, and Jerome L. Kaufman. “The Food System: A Stranger to the Planning Field.” Journal of the American Planning Association 66, no. 2 (June 30, 2000): 113–24. https://doi.org/10.1080/01944360008976093.↩︎

  13. https://github.com/CSU-Local-and-Regional-Food-Systems/USDA-AMS-Data-and-Metrics/tree/main↩︎

  14. U.S. Census Bureau. (n.d.). SAIPE State and County Estimates for 2022. Retrieved March 27, 2024, from https://data.census.gov/↩︎

  15. 2022 Census of Agriculture. QuickStats, State and County Data. [Washington, D.C.] :United States Department of Agriculture, National Agricultural Statistics Service, 2014.↩︎

  16. https://www.consumerfinance.gov/rural-or-underserved-tool/↩︎

  17. https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/↩︎